title: Self‑Reinforcing Generative Learning (SRGL)

Self‑Reinforcing Generative Learning (SRGL)

SRGL is a neuroscience‑inspired framework (GAN‑like) that ingests raw activity and curated intelligence to generate improved detections and policies, iteratively optimized via outcome feedback.

The SRGL Loop

  1. Observe — Endpoint senses high‑value behaviors (process, file, network, identity, OS artifacts) and emits compact, structured signals to LTM; originals are preserved for audit.
  2. Propose — Generative models synthesize candidate behavior profiles, policies, and enrichment rationales.
  3. Validate — Test against recent/historical telemetry; produce evidence trails; calibrate confidence.
  4. Reinforce — Human feedback, model outcomes, and operational impact strengthen or weaken proposals (weak supervision, pseudo‑labels, active learning).
  5. Deploy & Govern — Activate validated policies with guardrails; monitor drift and calibration continuously.

Result

A system that learns faster with every interaction, turning expert judgment and real‑world outcomes into durable capability.